{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n",
    "<div class=\"align-center\">\n",
    "<a href=\"https://unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
    "<a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n",
    "<a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a></a> Join Discord if you need help + \u2b50 <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> \u2b50\n",
    "</div>\n",
    "\n",
    "To install Unsloth your local device, follow [our guide](https://docs.unsloth.ai/get-started/install-and-update). This notebook is licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme).\n",
    "\n",
    "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### News"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Introducing FP8 precision training for faster RL inference. [Read Blog](https://docs.unsloth.ai/new/fp8-reinforcement-learning).\n",
    "\n",
    "Unsloth's [Docker image](https://hub.docker.com/r/unsloth/unsloth) is here! Start training with no setup & environment issues. [Read our Guide](https://docs.unsloth.ai/new/how-to-train-llms-with-unsloth-and-docker).\n",
    "\n",
    "[gpt-oss RL](https://docs.unsloth.ai/new/gpt-oss-reinforcement-learning) is now supported with the fastest inference & lowest VRAM. Try our [new notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb) which creates kernels!\n",
    "\n",
    "Introducing [Vision](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) and [Standby](https://docs.unsloth.ai/basics/memory-efficient-rl) for RL! Train Qwen, Gemma etc. VLMs with GSPO - even faster with less VRAM.\n",
    "\n",
    "Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": "%%capture\nimport os\n\n!pip install pip3-autoremove\n!pip install torch torchvision torchaudio xformers --index-url https://download.pytorch.org/whl/cu128\n!pip install unsloth\n!pip install transformers==4.56.2\n!pip install --no-deps trl==0.22.2\n!pip install jiwer\n!pip install einops addict easydict"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9xLDGk41C7IF"
   },
   "source": [
    "### Unsloth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's prepare the OCR model to our local first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from huggingface_hub import snapshot_download\n",
    "snapshot_download(\"unsloth/DeepSeek-OCR\", local_dir = \"deepseek_ocr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QmUBVEnvCDJv",
    "outputId": "f60d1c33-d54d-4d4d-b7ec-6591e219fc67"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\ud83e\udda5 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
      "\ud83e\udda5 Unsloth Zoo will now patch everything to make training faster!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are using a model of type deepseek_vl_v2 to instantiate a model of type DeepseekOCR. This is not supported for all configurations of models and can yield errors.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unsloth: WARNING `trust_remote_code` is True.\n",
      "Are you certain you want to do remote code execution?\n",
      "==((====))==  Unsloth 2025.11.1: Fast Deepseekocr patching. Transformers: 4.56.2.\n",
      "   \\\\   /|    NVIDIA A100-SXM4-40GB. Num GPUs = 1. Max memory: 39.557 GB. Platform: Linux.\n",
      "O^O/ \\_/ \\    Torch: 2.8.0+cu126. CUDA: 8.0. CUDA Toolkit: 12.6. Triton: 3.4.0\n",
      "\\        /    Bfloat16 = TRUE. FA [Xformers = 0.0.32.post2. FA2 = False]\n",
      " \"-____-\"     Free license: http://github.com/unslothai/unsloth\n",
      "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are using a model of type deepseek_vl_v2 to instantiate a model of type DeepseekOCR. This is not supported for all configurations of models and can yield errors.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unsloth: QLoRA and full finetuning all not selected. Switching to 16bit LoRA.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of DeepseekOCRForCausalLM were not initialized from the model checkpoint at ./deepseek_ocr and are newly initialized: ['model.vision_model.embeddings.position_ids']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "from unsloth import FastVisionModel # FastLanguageModel for LLMs\n",
    "import torch\n",
    "from transformers import AutoModel\n",
    "import os\n",
    "os.environ[\"UNSLOTH_WARN_UNINITIALIZED\"] = '0'\n",
    "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n",
    "fourbit_models = [\n",
    "    \"unsloth/Qwen3-VL-8B-Instruct-bnb-4bit\", # Qwen 3 vision support\n",
    "    \"unsloth/Qwen3-VL-8B-Thinking-bnb-4bit\",\n",
    "    \"unsloth/Qwen3-VL-32B-Instruct-bnb-4bit\",\n",
    "    \"unsloth/Qwen3-VL-32B-Thinking-bnb-4bit\",\n",
    "] # More models at https://huggingface.co/unsloth\n",
    "\n",
    "model, tokenizer = FastVisionModel.from_pretrained(\n",
    "    \"./deepseek_ocr\",\n",
    "    load_in_4bit = False, # Use 4bit to reduce memory use. False for 16bit LoRA.\n",
    "    auto_model = AutoModel,\n",
    "    trust_remote_code=True,\n",
    "    unsloth_force_compile=True,\n",
    "    use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for long context\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "id": "n4pFWSRAqSfB"
   },
   "outputs": [],
   "source": [
    "# @title Create evaluation functions\n",
    "\n",
    "import json\n",
    "import os\n",
    "from typing import Dict\n",
    "import numpy as np\n",
    "from jiwer import cer\n",
    "from tqdm import tqdm\n",
    "from datasets import load_dataset\n",
    "\n",
    "\n",
    "def calculate_cer(ref: str, hyp: str) -> float:\n",
    "    \"\"\"Helper to calculate CER and convert to percentage.\"\"\"\n",
    "    return cer(ref, hyp) * 100\n",
    "\n",
    "\n",
    "def evaluate_model(\n",
    "    model,\n",
    "    tokenizer,\n",
    "    dataset,\n",
    "    num_samples: int = 100,\n",
    "    base_size: int = 1024,\n",
    "    image_size: int = 640,\n",
    "    crop_mode: bool = True,\n",
    "    verbose: bool = True\n",
    "):\n",
    "    \"\"\"\n",
    "    Runs the model over a subset of the dataset to see how it performs.\n",
    "    It'll calculate CER stats and save all the predictions.\n",
    "    \"\"\"\n",
    "\n",
    "    results = {\n",
    "        'cer_scores': [],\n",
    "        'predictions': [],\n",
    "        'references': [],\n",
    "        'sample_indices': []\n",
    "    }\n",
    "\n",
    "    # make sure we don't try to sample more than we have\n",
    "    num_samples = min(num_samples, len(dataset))\n",
    "\n",
    "    # Grab evenly spaced samples from the dataset\n",
    "    indices = np.linspace(0, len(dataset) - 1, num_samples, dtype=int)\n",
    "\n",
    "    # Use tqdm for a progress bar if verbose\n",
    "    iterator = tqdm(indices, desc=\"Evaluating\") if verbose else indices\n",
    "\n",
    "    for idx in iterator:\n",
    "        sample = dataset[int(idx)]\n",
    "\n",
    "        # The model.infer method needs a file path, so we save a temp image\n",
    "        temp_image_path = f\"temp_eval_image_{idx}.jpg\"\n",
    "        sample['image_path'].save(temp_image_path)\n",
    "\n",
    "        prediction = \"\"\n",
    "        reference = sample[\"text\"].strip()\n",
    "\n",
    "        try:\n",
    "            # Run the actual inference\n",
    "            prediction = model.infer(\n",
    "                tokenizer,\n",
    "                prompt=\"<image>\\nFree OCR. \",\n",
    "                image_file=temp_image_path,\n",
    "                output_path=\"temp_output\",\n",
    "                base_size=base_size,\n",
    "                image_size=image_size,\n",
    "                crop_mode=crop_mode,\n",
    "                eval_mode=True,\n",
    "                save_results=False,\n",
    "                test_compress=False\n",
    "            )\n",
    "\n",
    "            prediction = prediction.strip()\n",
    "\n",
    "            # Calculate CER\n",
    "            cer_score = calculate_cer(reference, prediction)\n",
    "\n",
    "            results['cer_scores'].append(cer_score)\n",
    "            results['predictions'].append(prediction)\n",
    "            results['references'].append(reference)\n",
    "            results['sample_indices'].append(int(idx))\n",
    "\n",
    "        except Exception as e:\n",
    "            # Don't let one bad sample crash the whole evaluation\n",
    "            print(f\"\\nError processing sample {idx}: {e}\")\n",
    "            print(f\"Reference was: {reference}\")\n",
    "            continue\n",
    "        finally:\n",
    "            # Clean up the temp file whether it succeeded or failed\n",
    "            if os.path.exists(temp_image_path):\n",
    "                os.remove(temp_image_path)\n",
    "\n",
    "    # Add summary stats\n",
    "    if results['cer_scores']:\n",
    "        results['mean_cer'] = np.mean(results['cer_scores'])\n",
    "        results['median_cer'] = np.median(results['cer_scores'])\n",
    "        results['std_cer'] = np.std(results['cer_scores'])\n",
    "        results['min_cer'] = np.min(results['cer_scores'])\n",
    "        results['max_cer'] = np.max(results['cer_scores'])\n",
    "    else:\n",
    "        print(\"Warning: No samples were successfully processed.\")\n",
    "        results['mean_cer'] = -1.0\n",
    "\n",
    "    results['num_samples'] = len(results['cer_scores'])\n",
    "\n",
    "    return results\n",
    "\n",
    "def print_evaluation_summary(results: Dict, title: str = \"Evaluation Results\"):\n",
    "    \"\"\"Prints a nice summary of the stats to the console.\"\"\"\n",
    "\n",
    "    print(\"\\n\" + \"=\"*60)\n",
    "    print(f\"{title}\")\n",
    "    print(\"=\"*60)\n",
    "    print(f\"Number of samples: {results['num_samples']}\")\n",
    "    print(f\"Mean CER: {results['mean_cer']:.2f}%\")\n",
    "    print(f\"Median CER: {results['median_cer']:.2f}%\")\n",
    "    print(f\"Std Dev: {results['std_cer']:.2f}%\")\n",
    "    print(f\"Min CER: {results['min_cer']:.2f}%\")\n",
    "    print(f\"Max CER: {results['max_cer']:.2f}%\")\n",
    "    print(\"=\"*60)\n",
    "\n",
    "    # Show best and worst examples\n",
    "    sorted_indices = np.argsort(results['cer_scores'])\n",
    "\n",
    "    print(\"\\n Best Predictions (Lowest CER):\")\n",
    "    for i in range(min(3, len(sorted_indices))):\n",
    "        idx = sorted_indices[i]\n",
    "        print(f\"\\nSample {results['sample_indices'][idx]} (CER: {results['cer_scores'][idx]:.2f}%)\")\n",
    "        print(f\"Reference:  {results['references'][idx][:100]}...\")\n",
    "        print(f\"Prediction: {results['predictions'][idx][:100]}...\")\n",
    "\n",
    "    print(\"\\n Worst Predictions (Highest CER):\")\n",
    "    for i in range(min(3, len(sorted_indices))):\n",
    "        idx = sorted_indices[-(i+1)]\n",
    "        print(f\"\\nSample {results['sample_indices'][idx]} (CER: {results['cer_scores'][idx]:.2f}%)\")\n",
    "        print(f\"Reference:  {results['references'][idx][:100]}...\")\n",
    "        print(f\"Prediction: {results['predictions'][idx][:100]}...\")\n",
    "\n",
    "def save_evaluation_results(results: Dict, filepath: str):\n",
    "    \"\"\"Save full results dictionary to a JSON file.\"\"\"\n",
    "    with open(filepath, 'w', encoding='utf-8') as f:\n",
    "        json.dump(results, f, ensure_ascii=False, indent=2)\n",
    "    print(f\"\\n\u2705 Results saved to {filepath}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JfZVh2ByxFNh"
   },
   "source": [
    "### Let's Evaluate Deepseek-OCR Baseline Performance on Persian Transcription"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
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    },
    "id": "9wCSpgCf4RHp",
    "outputId": "d0590c2c-b939-42b8-aa10-0bd59d451d91"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading evaluation dataset...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d45817dc370847879552ca300baf03bd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "README.md:   0%|          | 0.00/967 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a5d3fad63f8a4042940c0d8961022e03",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "data/train-00000-of-00002.parquet:   0%|          | 0.00/255M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c4195e483ec443e7837e86002a27044c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "data/train-00001-of-00002.parquet:   0%|          | 0.00/256M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0a6d77eca7864737907dd6071c41d965",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "data/test-00000-of-00001.parquet:   0%|          | 0.00/57.1M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b959981adf4b42a79913b8af7328416f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/179999 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "52fb2f0ff7f24851b041bd00b50be26a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating test split:   0%|          | 0/20000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Running Baseline Evaluation...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating:   0%|          | 0/200 [00:00<?, ?it/s]The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
      "Evaluating: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [03:12<00:00,  1.04it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "Baseline Model Performance\n",
      "============================================================\n",
      "Number of samples: 200\n",
      "Mean CER: 149.07%\n",
      "Median CER: 80.00%\n",
      "Std Dev: 310.39%\n",
      "Min CER: 0.00%\n",
      "Max CER: 3500.00%\n",
      "============================================================\n",
      "\n",
      " Best Predictions (Lowest CER):\n",
      "\n",
      "Sample 5024 (CER: 0.00%)\n",
      "Reference:  \u0686\u0648\u0646 \u0647\u0633\u062a\u06cc \u062e\u06cc\u0644\u06cc \u0632\u06cc\u0627\u062f...\n",
      "Prediction: \u0686\u0648\u0646 \u0647\u0633\u062a\u06cc \u062e\u06cc\u0644\u06cc \u0632\u06cc\u0627\u062f...\n",
      "\n",
      "Sample 3517 (CER: 0.00%)\n",
      "Reference:  \u062a\u0648 \u0627\u06cc\u0631\u0627\u0646 \u0647\u06cc\u0686\u0648\u0642\u062a \u0627\u0632 \u0627\u06cc\u0646\u0647\u0627 \u0648\u062c\u0648\u062f \u0646\u062e\u0648\u0627\u0647\u062f \u062f\u0627\u0634\u062a...\n",
      "Prediction: \u062a\u0648 \u0627\u06cc\u0631\u0627\u0646 \u0647\u06cc\u0686\u0648\u0642\u062a \u0627\u0632 \u0627\u06cc\u0646\u0647\u0627 \u0648\u062c\u0648\u062f \u0646\u062e\u0648\u0627\u0647\u062f \u062f\u0627\u0634\u062a...\n",
      "\n",
      "Sample 9949 (CER: 0.00%)\n",
      "Reference:  \u06a9\u0627\u0634 \u0645\u06cc\u062f\u0648\u0646\u0633\u062a\u0645 \u0647\u06cc\u0686\u06cc \u0628\u06cc\u062e\u06cc\u0627\u0644...\n",
      "Prediction: \u06a9\u0627\u0634 \u0645\u06cc\u062f\u0648\u0646\u0633\u062a\u0645 \u0647\u06cc\u0686\u06cc \u0628\u06cc\u062e\u06cc\u0627\u0644...\n",
      "\n",
      " Worst Predictions (Highest CER):\n",
      "\n",
      "Sample 11155 (CER: 3500.00%)\n",
      "Reference:  \u062e\u0633\u0648...\n",
      "Prediction: \\[ \\text{CH}_3\\text{CH}_2\\text{CH}_2\\text{CH}_2\\text{CH}_2\\text{CH}_2\\text{CH}_2\\text{CH}_2\\text{CH}...\n",
      "\n",
      "Sample 13366 (CER: 1900.00%)\n",
      "Reference:  \u0645\u0634\u0648...\n",
      "Prediction: \\[\\begin{align*}\\underline{\\mathfrak{su}}_0\\end{align*}\\]...\n",
      "\n",
      "Sample 10552 (CER: 1014.29%)\n",
      "Reference:  \u0647\u06cc\u06cc\u06cc\u06cc\u06cc\u0686...\n",
      "Prediction: e\n",
      "e\n",
      "e\n",
      "e\n",
      "e\n",
      "e\n",
      "e\n",
      "e\n",
      "e\n",
      "e\n",
      "e\n",
      "e\n",
      "e\n",
      "e\n",
      "e\n",
      "e\n",
      "e\n",
      "e\n",
      "o\n",
      "o\n",
      "o\n",
      "o\n",
      "o\n",
      "o\n",
      "o\n",
      "o\n",
      "o\n",
      "o\n",
      "o\n",
      "o\n",
      "o\n",
      "o\n",
      "o\n",
      "o\n",
      "o\n",
      "o...\n",
      "\n",
      "\u2705 Results saved to baseline_evaluation.json\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Loading evaluation dataset...\")\n",
    "from datasets import load_dataset\n",
    "\n",
    "eval_dataset = load_dataset(\"hezarai/parsynth-ocr-200k\", split=\"test\")\n",
    "\n",
    "print(\"\\n Running Baseline Evaluation...\")\n",
    "baseline_results = evaluate_model(\n",
    "    model=model,\n",
    "    tokenizer=tokenizer,\n",
    "    dataset=eval_dataset,\n",
    "    num_samples=200,\n",
    "    base_size=1024,\n",
    "    image_size=640,\n",
    "    crop_mode=True,\n",
    "    verbose=True\n",
    ")\n",
    "\n",
    "print_evaluation_summary(baseline_results, \"Baseline Model Performance\")\n",
    "save_evaluation_results(baseline_results, \"baseline_evaluation.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-6LYHC3F0Ihf"
   },
   "source": [
    "<h3>Mean Baseline Model Performance: 149.07% Character Error Rate (CER) for this eval set !</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fzckMII_02s_"
   },
   "source": [
    "# Let's finetune Deepseek-OCR !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SXd9bTZd1aaL"
   },
   "source": [
    "We now add LoRA adapters for parameter efficient finetuning - this allows us to only efficiently train 1% of all parameters.\n",
    "\n",
    "**[NEW]** We also support finetuning ONLY the vision part of the model, or ONLY the language part. Or you can select both! You can also select to finetune the attention or the MLP layers!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6bZsfBuZDeCL",
    "outputId": "031d3543-ae74-4cbe-ab6a-486e29067985"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unsloth: Making `model.base_model.model.model` require gradients\n"
     ]
    }
   ],
   "source": [
    "model = FastVisionModel.get_peft_model(\n",
    "    model,\n",
    "    target_modules=[\n",
    "        \"q_proj\",\n",
    "        \"k_proj\",\n",
    "        \"v_proj\",\n",
    "        \"o_proj\",\n",
    "        \"gate_proj\",\n",
    "        \"up_proj\",\n",
    "        \"down_proj\",\n",
    "    ],\n",
    "\n",
    "    r = 16,           # The larger, the higher the accuracy, but might overfit\n",
    "    lora_alpha = 16,  # Recommended alpha == r at least\n",
    "    lora_dropout = 0,\n",
    "    bias = \"none\",\n",
    "    random_state = 3407,\n",
    "    use_rslora = False,  # We support rank stabilized LoRA\n",
    "    loftq_config = None, # And LoftQ\n",
    "    # target_modules = \"all-linear\", # Optional now! Can specify a list if needed\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vITh0KVJ10qX"
   },
   "source": [
    "<a name=\"Data\"></a>\n",
    "### Data Prep\n",
    "We'll be using a dataset for Persian OCR. The goal is to convert these images into a computer readable form - ie text. This can be very useful for digitizing Persian text.\n",
    "\n",
    "You can access the dataset [here](https://huggingface.co/datasets/hezarai/parsynth-ocr-200k).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W1W2Qhsz6rUT"
   },
   "source": [
    "Let's take an overview look at the dataset. We shall see what the 3rd image is, and what caption it had."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K9CBpiISFa6C"
   },
   "source": [
    "To format the dataset, all vision finetuning tasks should be formatted as follows:\n",
    "\n",
    "```python\n",
    "[\n",
    "{ \"role\": \"<|User|>\",\n",
    "  \"content\": \"\",\n",
    "  \"images\": []\n",
    "},\n",
    "{ \"role\": \"<|Assistant|>\",\n",
    "  \"content\": \"\"\n",
    "},\n",
    "]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oPXzJZzHEgXe"
   },
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "instruction = \"<image>\\nFree OCR. \"\n",
    "\n",
    "def convert_to_conversation(sample):\n",
    "    \"\"\"Convert dataset sample to conversation format\"\"\"\n",
    "    conversation = [\n",
    "        {\n",
    "            \"role\": \"<|User|>\",\n",
    "            \"content\": instruction,\n",
    "            \"images\": [sample['image']]\n",
    "        },\n",
    "        {\n",
    "            \"role\": \"<|Assistant|>\",\n",
    "            \"content\": sample[\"text\"]\n",
    "        },\n",
    "    ]\n",
    "    return {\"messages\": conversation}\n",
    "\n",
    "# Load dataset\n",
    "dataset = load_dataset(\"hezarai/parsynth-ocr-200k\", split = \"train[:1000]\")\n",
    "dataset = dataset.rename_column(\"image_path\", \"image\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FY-9u-OD6_gE"
   },
   "source": [
    "Let's convert the dataset into the \"correct\" format for finetuning:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "NLHM0PGn30x5"
   },
   "outputs": [],
   "source": [
    "converted_dataset = [convert_to_conversation(sample) for sample in dataset]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ndDUB23CGAC5"
   },
   "source": [
    "We look at how the conversations are structured for the first example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gGFzmplrEy9I",
    "outputId": "5e3bcd1c-4273-4a25-ebeb-0ccd51cce81b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'messages': [{'role': '<|User|>',\n",
       "   'content': '<image>\\nFree OCR. ',\n",
       "   'images': [<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=218x48>]},\n",
       "  {'role': '<|Assistant|>', 'content': '\u0647\u0645\u0647\u0627\u0634 \u062c\u0628\u0631\u0647 \u0648 \u0627\u062e\u062a\u06cc\u0627\u0631 \u062a\u0648\u0647\u0645\u0647'}]}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "converted_dataset[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "id": "E2WR-p20LcG_"
   },
   "outputs": [],
   "source": [
    "# @title Create datacollator\n",
    "\n",
    "import torch\n",
    "import math\n",
    "from dataclasses import dataclass\n",
    "from typing import Dict, List, Any, Tuple\n",
    "from PIL import Image, ImageOps\n",
    "from torch.nn.utils.rnn import pad_sequence\n",
    "import io\n",
    "\n",
    "from deepseek_ocr.modeling_deepseekocr import (\n",
    "    format_messages,\n",
    "    text_encode,\n",
    "    BasicImageTransform,\n",
    "    dynamic_preprocess,\n",
    ")\n",
    "\n",
    "@dataclass\n",
    "class DeepSeekOCRDataCollator:\n",
    "    \"\"\"\n",
    "    Data collator that handles image preprocessing and tokenization at batch time.\n",
    "\n",
    "    Args:\n",
    "        tokenizer: Tokenizer instance\n",
    "        model: Model instance (used to get dtype)\n",
    "        image_size: Size for image patches (default: 640)\n",
    "        base_size: Size for global view (default: 1024)\n",
    "        crop_mode: Whether to use dynamic cropping for large images\n",
    "        train_on_responses_only: If True, only train on assistant responses (mask user prompts)\n",
    "    \"\"\"\n",
    "    tokenizer: Any\n",
    "    model: Any\n",
    "    image_size: int = 640\n",
    "    base_size: int = 1024\n",
    "    crop_mode: bool = True\n",
    "    image_token_id: int = 128815\n",
    "    train_on_responses_only: bool = True\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        tokenizer,\n",
    "        model,\n",
    "        image_size: int = 640,\n",
    "        base_size: int = 1024,\n",
    "        crop_mode: bool = True,\n",
    "        train_on_responses_only: bool = True,\n",
    "    ):\n",
    "        self.tokenizer = tokenizer\n",
    "        self.model = model\n",
    "        self.image_size = image_size\n",
    "        self.base_size = base_size\n",
    "        self.crop_mode = crop_mode\n",
    "        self.image_token_id = 128815\n",
    "        self.dtype = model.dtype  # Get dtype from model\n",
    "        self.train_on_responses_only = train_on_responses_only\n",
    "\n",
    "        self.image_transform = BasicImageTransform(\n",
    "            mean=(0.5, 0.5, 0.5),\n",
    "            std=(0.5, 0.5, 0.5),\n",
    "            normalize=True\n",
    "        )\n",
    "        self.patch_size = 16\n",
    "        self.downsample_ratio = 4\n",
    "\n",
    "        # Get BOS token ID from tokenizer\n",
    "        if hasattr(tokenizer, 'bos_token_id') and tokenizer.bos_token_id is not None:\n",
    "            self.bos_id = tokenizer.bos_token_id\n",
    "        else:\n",
    "            self.bos_id = 0\n",
    "            print(f\"Warning: tokenizer has no bos_token_id, using default: {self.bos_id}\")\n",
    "\n",
    "    def deserialize_image(self, image_data) -> Image.Image:\n",
    "        \"\"\"Convert image data (bytes dict or PIL Image) to PIL Image in RGB mode\"\"\"\n",
    "        if isinstance(image_data, Image.Image):\n",
    "            return image_data.convert(\"RGB\")\n",
    "        elif isinstance(image_data, dict) and 'bytes' in image_data:\n",
    "            image_bytes = image_data['bytes']\n",
    "            image = Image.open(io.BytesIO(image_bytes))\n",
    "            return image.convert(\"RGB\")\n",
    "        else:\n",
    "            raise ValueError(f\"Unsupported image format: {type(image_data)}\")\n",
    "\n",
    "    def calculate_image_token_count(self, image: Image.Image, crop_ratio: Tuple[int, int]) -> int:\n",
    "        \"\"\"Calculate the number of tokens this image will generate\"\"\"\n",
    "        num_queries = math.ceil((self.image_size // self.patch_size) / self.downsample_ratio)\n",
    "        num_queries_base = math.ceil((self.base_size // self.patch_size) / self.downsample_ratio)\n",
    "\n",
    "        width_crop_num, height_crop_num = crop_ratio\n",
    "\n",
    "        if self.crop_mode:\n",
    "            img_tokens = num_queries_base * num_queries_base + 1\n",
    "            if width_crop_num > 1 or height_crop_num > 1:\n",
    "                img_tokens += (num_queries * width_crop_num + 1) * (num_queries * height_crop_num)\n",
    "        else:\n",
    "            img_tokens = num_queries * num_queries + 1\n",
    "\n",
    "        return img_tokens\n",
    "\n",
    "    def process_image(self, image: Image.Image) -> Tuple[List, List, List, List, Tuple[int, int]]:\n",
    "        \"\"\"\n",
    "        Process a single image based on crop_mode and size thresholds\n",
    "\n",
    "        Returns:\n",
    "            Tuple of (images_list, images_crop_list, images_spatial_crop, tokenized_image, crop_ratio)\n",
    "        \"\"\"\n",
    "        images_list = []\n",
    "        images_crop_list = []\n",
    "        images_spatial_crop = []\n",
    "\n",
    "        if self.crop_mode:\n",
    "            # Determine crop ratio based on image size\n",
    "            if image.size[0] <= 640 and image.size[1] <= 640:\n",
    "                crop_ratio = (1, 1)\n",
    "                images_crop_raw = []\n",
    "            else:\n",
    "                images_crop_raw, crop_ratio = dynamic_preprocess(\n",
    "                    image, min_num=2, max_num=9,\n",
    "                    image_size=self.image_size, use_thumbnail=False\n",
    "                )\n",
    "\n",
    "            # Process global view with padding\n",
    "            global_view = ImageOps.pad(\n",
    "                image, (self.base_size, self.base_size),\n",
    "                color=tuple(int(x * 255) for x in self.image_transform.mean)\n",
    "            )\n",
    "            images_list.append(self.image_transform(global_view).to(self.dtype))\n",
    "\n",
    "            width_crop_num, height_crop_num = crop_ratio\n",
    "            images_spatial_crop.append([width_crop_num, height_crop_num])\n",
    "\n",
    "            # Process local views (crops) if applicable\n",
    "            if width_crop_num > 1 or height_crop_num > 1:\n",
    "                for crop_img in images_crop_raw:\n",
    "                    images_crop_list.append(\n",
    "                        self.image_transform(crop_img).to(self.dtype)\n",
    "                    )\n",
    "\n",
    "            # Calculate image tokens\n",
    "            num_queries = math.ceil((self.image_size // self.patch_size) / self.downsample_ratio)\n",
    "            num_queries_base = math.ceil((self.base_size // self.patch_size) / self.downsample_ratio)\n",
    "\n",
    "            tokenized_image = ([self.image_token_id] * num_queries_base + [self.image_token_id]) * num_queries_base\n",
    "            tokenized_image += [self.image_token_id]\n",
    "\n",
    "            if width_crop_num > 1 or height_crop_num > 1:\n",
    "                tokenized_image += ([self.image_token_id] * (num_queries * width_crop_num) + [self.image_token_id]) * (\n",
    "                    num_queries * height_crop_num)\n",
    "\n",
    "        else:  # crop_mode = False\n",
    "            crop_ratio = (1, 1)\n",
    "            images_spatial_crop.append([1, 1])\n",
    "\n",
    "            # For smaller base sizes, resize; for larger, pad\n",
    "            if self.base_size <= 640:\n",
    "                resized_image = image.resize((self.base_size, self.base_size), Image.LANCZOS)\n",
    "                images_list.append(self.image_transform(resized_image).to(self.dtype))\n",
    "            else:\n",
    "                global_view = ImageOps.pad(\n",
    "                    image, (self.base_size, self.base_size),\n",
    "                    color=tuple(int(x * 255) for x in self.image_transform.mean)\n",
    "                )\n",
    "                images_list.append(self.image_transform(global_view).to(self.dtype))\n",
    "\n",
    "            num_queries = math.ceil((self.base_size // self.patch_size) / self.downsample_ratio)\n",
    "            tokenized_image = ([self.image_token_id] * num_queries + [self.image_token_id]) * num_queries\n",
    "            tokenized_image += [self.image_token_id]\n",
    "\n",
    "        return images_list, images_crop_list, images_spatial_crop, tokenized_image, crop_ratio\n",
    "\n",
    "    def process_single_sample(self, messages: List[Dict]) -> Dict[str, Any]:\n",
    "            \"\"\"\n",
    "            Process a single conversation into model inputs.\n",
    "\n",
    "            This version builds the token sequence in a single pass,\n",
    "            accurately calculating the prompt/response split point.\n",
    "            \"\"\"\n",
    "\n",
    "            # --- 1. Setup ---\n",
    "            images = []\n",
    "            for message in messages:\n",
    "                if \"images\" in message and message[\"images\"]:\n",
    "                    for img_data in message[\"images\"]:\n",
    "                        if img_data is not None:\n",
    "                            pil_image = self.deserialize_image(img_data)\n",
    "                            images.append(pil_image)\n",
    "\n",
    "            if not images:\n",
    "                raise ValueError(\"No images found in sample. Please ensure all samples contain images.\")\n",
    "\n",
    "            tokenized_str = []\n",
    "            images_seq_mask = []\n",
    "            images_list, images_crop_list, images_spatial_crop = [], [], []\n",
    "\n",
    "            prompt_token_count = -1 # Index to start training\n",
    "            assistant_started = False\n",
    "            image_idx = 0\n",
    "\n",
    "            # Add BOS token at the very beginning\n",
    "            tokenized_str.append(self.bos_id)\n",
    "            images_seq_mask.append(False)\n",
    "\n",
    "            for message in messages:\n",
    "                role = message[\"role\"]\n",
    "                content = message[\"content\"]\n",
    "\n",
    "                # Check if this is the assistant's turn\n",
    "                if role == \"<|Assistant|>\":\n",
    "                    if not assistant_started:\n",
    "                        # This is the split point. All tokens added *so far*\n",
    "                        # are part of the prompt.\n",
    "                        prompt_token_count = len(tokenized_str)\n",
    "                        assistant_started = True\n",
    "\n",
    "                    # Append the EOS token string to the *end* of assistant content\n",
    "                    content = f\"{content.strip()} {self.tokenizer.eos_token}\"\n",
    "\n",
    "                # Split this message's content by the image token\n",
    "                text_splits = content.split('<image>')\n",
    "\n",
    "                for i, text_sep in enumerate(text_splits):\n",
    "                    # Tokenize the text part\n",
    "                    tokenized_sep = text_encode(self.tokenizer, text_sep, bos=False, eos=False)\n",
    "                    tokenized_str.extend(tokenized_sep)\n",
    "                    images_seq_mask.extend([False] * len(tokenized_sep))\n",
    "\n",
    "                    # If this text is followed by an <image> tag\n",
    "                    if i < len(text_splits) - 1:\n",
    "                        if image_idx >= len(images):\n",
    "                            raise ValueError(\n",
    "                                f\"Data mismatch: Found '<image>' token but no corresponding image.\"\n",
    "                            )\n",
    "\n",
    "                        # Process the image\n",
    "                        image = images[image_idx]\n",
    "                        img_list, crop_list, spatial_crop, tok_img, _ = self.process_image(image)\n",
    "\n",
    "                        images_list.extend(img_list)\n",
    "                        images_crop_list.extend(crop_list)\n",
    "                        images_spatial_crop.extend(spatial_crop)\n",
    "\n",
    "                        # Add image placeholder tokens\n",
    "                        tokenized_str.extend(tok_img)\n",
    "                        images_seq_mask.extend([True] * len(tok_img))\n",
    "\n",
    "                        image_idx += 1 # Move to the next image\n",
    "\n",
    "            # --- 3. Validation and Final Prep ---\n",
    "            if image_idx != len(images):\n",
    "                raise ValueError(\n",
    "                    f\"Data mismatch: Found {len(images)} images but only {image_idx} '<image>' tokens were used.\"\n",
    "                )\n",
    "\n",
    "            # If we never found an assistant message, we're in a weird state\n",
    "            # (e.g., user-only prompt). We mask everything.\n",
    "            if not assistant_started:\n",
    "                print(\"Warning: No assistant message found in sample. Masking all tokens.\")\n",
    "                prompt_token_count = len(tokenized_str)\n",
    "\n",
    "            # Prepare image tensors\n",
    "            images_ori = torch.stack(images_list, dim=0)\n",
    "            images_spatial_crop_tensor = torch.tensor(images_spatial_crop, dtype=torch.long)\n",
    "\n",
    "            if images_crop_list:\n",
    "                images_crop = torch.stack(images_crop_list, dim=0)\n",
    "            else:\n",
    "                images_crop = torch.zeros((1, 3, self.base_size, self.base_size), dtype=self.dtype)\n",
    "\n",
    "            return {\n",
    "                \"input_ids\": torch.tensor(tokenized_str, dtype=torch.long),\n",
    "                \"images_seq_mask\": torch.tensor(images_seq_mask, dtype=torch.bool),\n",
    "                \"images_ori\": images_ori,\n",
    "                \"images_crop\": images_crop,\n",
    "                \"images_spatial_crop\": images_spatial_crop_tensor,\n",
    "                \"prompt_token_count\": prompt_token_count, # This is now accurate\n",
    "            }\n",
    "\n",
    "    def __call__(self, features: List[Dict[str, Any]]) -> Dict[str, torch.Tensor]:\n",
    "        \"\"\"Collate batch of samples\"\"\"\n",
    "        batch_data = []\n",
    "\n",
    "        # Process each sample\n",
    "        for feature in features:\n",
    "            try:\n",
    "                processed = self.process_single_sample(feature['messages'])\n",
    "                batch_data.append(processed)\n",
    "            except Exception as e:\n",
    "                print(f\"Error processing sample: {e}\")\n",
    "                continue\n",
    "\n",
    "        if not batch_data:\n",
    "            raise ValueError(\"No valid samples in batch\")\n",
    "\n",
    "        # Extract lists\n",
    "        input_ids_list = [item['input_ids'] for item in batch_data]\n",
    "        images_seq_mask_list = [item['images_seq_mask'] for item in batch_data]\n",
    "        prompt_token_counts = [item['prompt_token_count'] for item in batch_data]\n",
    "\n",
    "        # Pad sequences\n",
    "        input_ids = pad_sequence(input_ids_list, batch_first=True, padding_value=self.tokenizer.pad_token_id)\n",
    "        images_seq_mask = pad_sequence(images_seq_mask_list, batch_first=True, padding_value=False)\n",
    "\n",
    "        # Create labels\n",
    "        labels = input_ids.clone()\n",
    "\n",
    "        # Mask padding tokens\n",
    "        labels[labels == self.tokenizer.pad_token_id] = -100\n",
    "\n",
    "        # Mask image tokens (model shouldn't predict these)\n",
    "        labels[images_seq_mask] = -100\n",
    "\n",
    "        # Mask user prompt tokens when train_on_responses_only=True (only train on assistant responses)\n",
    "        if self.train_on_responses_only:\n",
    "            for idx, prompt_count in enumerate(prompt_token_counts):\n",
    "                if prompt_count > 0:\n",
    "                    labels[idx, :prompt_count] = -100\n",
    "\n",
    "        # Create attention mask\n",
    "        attention_mask = (input_ids != self.tokenizer.pad_token_id).long()\n",
    "\n",
    "        # Prepare images batch (list of tuples)\n",
    "        images_batch = []\n",
    "        for item in batch_data:\n",
    "            images_batch.append((item['images_crop'], item['images_ori']))\n",
    "\n",
    "        # Stack spatial crop info\n",
    "        images_spatial_crop = torch.cat([item['images_spatial_crop'] for item in batch_data], dim=0)\n",
    "\n",
    "        return {\n",
    "            \"input_ids\": input_ids,\n",
    "            \"attention_mask\": attention_mask,\n",
    "            \"labels\": labels,\n",
    "            \"images\": images_batch,\n",
    "            \"images_seq_mask\": images_seq_mask,\n",
    "            \"images_spatial_crop\": images_spatial_crop,\n",
    "        }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "idAEIeSQ3xdS"
   },
   "source": [
    "<a name=\"Train\"></a>\n",
    "### Train the model\n",
    "Now let's train our model. We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!\n",
    "\n",
    "We use our new `DeepSeekOCRDataCollator` which will help in our vision finetuning setup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "95_Nn-89DhsL",
    "outputId": "f25b0da6-ad89-4255-b81c-e6ebbba77b55"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipython-input-4060472171.py:12: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n",
      "  trainer = Trainer(\n"
     ]
    }
   ],
   "source": [
    "from transformers import Trainer, TrainingArguments\n",
    "from unsloth import is_bf16_supported\n",
    "FastVisionModel.for_training(model) # Enable for training!\n",
    "data_collator = DeepSeekOCRDataCollator(\n",
    "    tokenizer=tokenizer,\n",
    "    model = model,\n",
    "    image_size=640,\n",
    "    base_size=1024,\n",
    "    crop_mode=True,\n",
    "    train_on_responses_only=True,\n",
    ")\n",
    "trainer = Trainer(\n",
    "    model = model,\n",
    "    tokenizer = tokenizer,\n",
    "    data_collator = data_collator, # Must use!\n",
    "    train_dataset = converted_dataset,\n",
    "    args = TrainingArguments(\n",
    "        per_device_train_batch_size = 2,\n",
    "        gradient_accumulation_steps = 4,\n",
    "        warmup_steps = 5,\n",
    "        max_steps = 60,\n",
    "        # num_train_epochs = 1, # Set this instead of max_steps for full training runs\n",
    "        learning_rate = 2e-4,\n",
    "        logging_steps = 1,\n",
    "        optim = \"adamw_8bit\",\n",
    "        weight_decay = 0.001,\n",
    "        lr_scheduler_type = \"linear\",\n",
    "        seed = 3407,\n",
    "        fp16 = not is_bf16_supported(),  # Use fp16 if bf16 is not supported\n",
    "        bf16 = is_bf16_supported(),  # Use bf16 if supported\n",
    "        output_dir = \"outputs\",\n",
    "        report_to = \"none\",     # For Weights and Biases\n",
    "        dataloader_num_workers=2,\n",
    "        # You MUST put the below items for vision finetuning:\n",
    "        remove_unused_columns = False,\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2ejIt2xSNKKp",
    "outputId": "88c63124-2589-457c-b6da-31d66aa85e34"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU = NVIDIA L4. Max memory = 22.161 GB.\n",
      "8.012 GB of memory reserved.\n"
     ]
    }
   ],
   "source": [
    "# @title Show current memory stats\n",
    "gpu_stats = torch.cuda.get_device_properties(0)\n",
    "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
    "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n",
    "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n",
    "print(f\"{start_gpu_memory} GB of memory reserved.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "yqxqAZ7KJ4oL",
    "outputId": "4a5d6d06-a894-4b1a-cd92-e50d9f7f029f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs used = 1\n",
      "   \\\\   /|    Num examples = 1,000 | Num Epochs = 1 | Total steps = 60\n",
      "O^O/ \\_/ \\    Batch size per device = 2 | Gradient accumulation steps = 4\n",
      "\\        /    Data Parallel GPUs = 1 | Total batch size (2 x 4 x 1) = 8\n",
      " \"-____-\"     Trainable parameters = 77,509,632 of 3,413,615,872 (2.27% trained)\n",
      "Unsloth: Not an error, but DeepseekOCRForCausalLM does not accept `num_items_in_batch`.\n",
      "Using gradient accumulation will be very slightly less accurate.\n",
      "Read more on gradient accumulation issues here: https://unsloth.ai/blog/gradient\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='60' max='60' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [60/60 10:09, Epoch 0/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4.054700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2.636700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3.507600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3.796600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2.209600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>4.074400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2.203600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.928200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>3.159700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>3.102700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2.546200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.717800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>3.042700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.606500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.880300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.979100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.849000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.423400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.525600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>2.193200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>3.213900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.234700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.728500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.795300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>2.064900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>2.581500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1.843000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.869800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1.531600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.970600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1.022600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.759500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>2.459400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>1.396900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>1.463400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>1.577700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>1.269700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1.552800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>1.643800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>2.084000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>1.611800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>1.941400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>1.341900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>1.808200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>1.672900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>1.228700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>2.254500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>1.669900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>1.170100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>1.966500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>1.498100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>1.387500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>1.434100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>2.174700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.994200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>1.131600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>2.537900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>1.550500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.868000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>1.361400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unsloth: Will smartly offload gradients to save VRAM!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are using a model of type deepseek_vl_v2 to instantiate a model of type DeepseekOCR. This is not supported for all configurations of models and can yield errors.\n"
     ]
    }
   ],
   "source": [
    "trainer_stats = trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AkwL1KhZNW1_"
   },
   "source": [
    "### Now after fine-tuning lets evaluate the model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "k3h5EB924Nwx",
    "outputId": "571ff8db-2b75-417a-e913-564c8a8a097d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [06:11<00:00,  1.86s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "Fine-tuned Model Performance\n",
      "============================================================\n",
      "Number of samples: 200\n",
      "Mean CER: 60.43%\n",
      "Median CER: 50.00%\n",
      "Std Dev: 80.63%\n",
      "Min CER: 0.00%\n",
      "Max CER: 916.67%\n",
      "============================================================\n",
      "\n",
      " Best Predictions (Lowest CER):\n",
      "\n",
      "Sample 301 (CER: 0.00%)\n",
      "Reference:  \u0628\u0627\u0634\u0647 \u0628\u0627\u0628\u0627 \u062a\u0648 \u0644\u0627\u06a9\u0686\u0631\u06cc\u060c \u062a\u0648 \u062e\u0627\u0635\u060c \u062a\u0648 \u062e\u0641\u0646...\n",
      "Prediction: \u0628\u0627\u0634\u0647 \u0628\u0627\u0628\u0627 \u062a\u0648 \u0644\u0627\u06a9\u0686\u0631\u06cc\u060c \u062a\u0648 \u062e\u0627\u0635\u060c \u062a\u0648 \u062e\u0641\u0646...\n",
      "\n",
      "Sample 2512 (CER: 0.00%)\n",
      "Reference:  \u0627\u0632 \u0634\u062e\u0635 \u062d\u0627\u062c \u0639\u0628\u062f\u0627\u0644\u0644\u0647 \u0632\u0646\u062c\u0628\u06cc\u0644\u06cc \u0645\u06cc\u06af\u06cc\u0631\u0646\u0634...\n",
      "Prediction: \u0627\u0632 \u0634\u062e\u0635 \u062d\u0627\u062c \u0639\u0628\u062f\u0627\u0644\u0644\u0647 \u0632\u0646\u062c\u0628\u06cc\u0644\u06cc \u0645\u06cc\u06af\u06cc\u0631\u0646\u0634...\n",
      "\n",
      "Sample 2713 (CER: 0.00%)\n",
      "Reference:  \u0646\u0645\u06cc \u062f\u0648\u0646\u0645 \u0648\u0627\u0644\u0627 \u062a\u062d\u0645\u0644 \u0646\u0642\u062f \u0646\u062f\u0627\u0631\u0646 \u0638\u0627\u0647\u0631\u0627...\n",
      "Prediction: \u0646\u0645\u06cc \u062f\u0648\u0646\u0645 \u0648\u0627\u0644\u0627 \u062a\u062d\u0645\u0644 \u0646\u0642\u062f \u0646\u062f\u0627\u0631\u0646 \u0638\u0627\u0647\u0631\u0627...\n",
      "\n",
      " Worst Predictions (Highest CER):\n",
      "\n",
      "Sample 14270 (CER: 916.67%)\n",
      "Reference:  \u06f4\u06f3\u06f5\u06f9\u06f4\u06f7\u06f4\u06f7\u06f3\u06f8\u06f9\u06f0...\n",
      "Prediction: \u067e\u0631\u0648\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631\u06cc\u067e\u0631...\n",
      "\n",
      "Sample 3919 (CER: 380.00%)\n",
      "Reference:  \u06f7\u06f5\u06f5\u06f0\u06f7\u06f1\u06f0\u06f6\u06f5\u06f9...\n",
      "Prediction: \u0648\u0627\u062f\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648\u0648...\n",
      "\n",
      "Sample 3718 (CER: 333.33%)\n",
      "Reference:  \u06f3\u06f2\u06f6\u06f7\u06f2\u06f2\u06f6\u06f5\u06f5\u06f8\u06f4\u06f6...\n",
      "Prediction: \u067e\u064f\u067e\u064f\u0633\u0648\u067e\u064f\u0633\u0648\u067e\u064f\u0633\u0648\u067e\u064f\u0633\u0648\u067e\u064f\u0633\u0648\u067e\u064f\u0633\u0648\u067e\u064f\u0633\u0648\u067e\u064f\u0633\u0648\u067e\u064f\u0633\u0648\u067e\u064f...\n",
      "\n",
      "\u2705 Results saved to finetuned_evaluation.json\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "FastVisionModel.for_inference(model) # Enable for inference!\n",
    "\n",
    "finetuned_results = evaluate_model(\n",
    "    model=model,\n",
    "    tokenizer=tokenizer,\n",
    "    dataset=eval_dataset,\n",
    "    num_samples=200,\n",
    "    base_size=1024,\n",
    "    image_size=640,\n",
    "    crop_mode=True,\n",
    "    verbose=True\n",
    ")\n",
    "\n",
    "print_evaluation_summary(finetuned_results, \"Fine-tuned Model Performance\")\n",
    "save_evaluation_results(finetuned_results, \"finetuned_evaluation.json\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MWBxcKXWOVD4"
   },
   "source": [
    "### Now lets compare both."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8gouaaWy86I0",
    "outputId": "7e66f946-9fea-43f6-af50-f2aab0595aaa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "\ud83d\udcc8 PERFORMANCE COMPARISON\n",
      "============================================================\n",
      "Baseline Mean CER:    149.07%\n",
      "Fine-tuned Mean CER:  60.43%\n",
      "\n",
      "\u2728 Absolute Improvement: 88.64%\n",
      "\u2728 Relative Improvement: 59.46%\n",
      "============================================================\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"\ud83d\udcc8 PERFORMANCE COMPARISON\")\n",
    "print(\"=\"*60)\n",
    "print(f\"Baseline Mean CER:    {baseline_results['mean_cer']:.2f}%\")\n",
    "print(f\"Fine-tuned Mean CER:  {finetuned_results['mean_cer']:.2f}%\")\n",
    "\n",
    "improvement = baseline_results['mean_cer'] - finetuned_results['mean_cer']\n",
    "relative_improvement = (improvement / baseline_results['mean_cer']) * 100\n",
    "\n",
    "print(f\"\\n\u2728 Absolute Improvement: {improvement:.2f}%\")\n",
    "print(f\"\u2728 Relative Improvement: {relative_improvement:.2f}%\")\n",
    "print(\"=\"*60)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z61qyZM6Odzk"
   },
   "source": [
    "With only 60 steps, we reduced the Character Error Rate (CER) from 149.07% to 60.43%, representing a 88.6% absolute improvement in character errors!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "id": "pCqnaKmlO1U9"
   },
   "outputs": [],
   "source": [
    "# @title Show final memory and time stats\n",
    "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
    "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n",
    "used_percentage = round(used_memory / max_memory * 100, 3)\n",
    "lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)\n",
    "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n",
    "print(\n",
    "    f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n",
    ")\n",
    "print(f\"Peak reserved memory = {used_memory} GB.\")\n",
    "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n",
    "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n",
    "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ekOmTR1hSNcr"
   },
   "source": [
    "<a name=\"Inference\"></a>\n",
    "### Inference\n",
    "Let's run the model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kR3gIAX-SM2q",
    "outputId": "03f74948-cfa1-4d39-83a6-7c8d59343f53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u067e\u0627\u06a9\u0633\u062a\u0627\u0646\n",
      "===============save results:===============\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "image: 0it [00:00, ?it/s]\n",
      "other: 0it [00:00, ?it/s]\n"
     ]
    }
   ],
   "source": [
    "eval_dataset[0]['image_path'].save('your_image.jpg')\n",
    "prompt = \"<image>\\nFree OCR. \"\n",
    "image_file = 'your_image.jpg'\n",
    "output_path = 'your/output/dir'\n",
    "\n",
    "# Tiny: base_size = 512, image_size = 512, crop_mode = False\n",
    "# Small: base_size = 640, image_size = 640, crop_mode = False\n",
    "# Base: base_size = 1024, image_size = 1024, crop_mode = False\n",
    "# Large: base_size = 1280, image_size = 1280, crop_mode = False\n",
    "\n",
    "# Gundam: base_size = 1024, image_size = 640, crop_mode = True\n",
    "\n",
    "res = model.infer(tokenizer, prompt=prompt, image_file=image_file,\n",
    "    output_path = output_path,\n",
    "    image_size=640,\n",
    "    base_size=1024,\n",
    "    crop_mode=True,\n",
    "    save_results = True,\n",
    "    test_compress = False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uMuVrWbjAzhc"
   },
   "source": [
    "<a name=\"Save\"></a>\n",
    "### Saving, loading finetuned models\n",
    "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n",
    "\n",
    "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "upcOlWe7A1vc",
    "outputId": "c2d31666-bd8f-4ee1-a48b-5b0f282b5d03"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are using a model of type deepseek_vl_v2 to instantiate a model of type DeepseekOCR. This is not supported for all configurations of models and can yield errors.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('lora_model/tokenizer_config.json',\n",
       " 'lora_model/special_tokens_map.json',\n",
       " 'lora_model/tokenizer.json')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.save_pretrained(\"lora_model\")  # Local saving\n",
    "tokenizer.save_pretrained(\"lora_model\")\n",
    "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n",
    "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AEEcJ4qfC7Lp"
   },
   "source": [
    "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MKX_XKs_BNZR"
   },
   "outputs": [],
   "source": [
    "if False:\n",
    "    from unsloth import FastVisionModel\n",
    "    model, tokenizer = FastVisionModel.from_pretrained(\n",
    "        model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
    "        load_in_4bit = False, # Use 4bit to reduce memory use. False for 16bit LoRA.\n",
    "        auto_model = AutoModel,\n",
    "        trust_remote_code=True,\n",
    "        unsloth_force_compile=True,\n",
    "        use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for long context\n",
    "    )\n",
    "    FastVisionModel.for_inference(model) # Enable for inference!\n",
    "\n",
    "prompt = \"<image>\\nFree OCR. \"\n",
    "image_file = 'your_image.jpg'\n",
    "output_path = 'your/output/dir'\n",
    "\n",
    "# Tiny: base_size = 512, image_size = 512, crop_mode = False\n",
    "# Small: base_size = 640, image_size = 640, crop_mode = False\n",
    "# Base: base_size = 1024, image_size = 1024, crop_mode = False\n",
    "# Large: base_size = 1280, image_size = 1280, crop_mode = False\n",
    "\n",
    "# Gundam: base_size = 1024, image_size = 640, crop_mode = True\n",
    "\n",
    "res = model.infer(tokenizer, prompt=prompt, image_file=image_file,\n",
    "    output_path = output_path,\n",
    "    image_size=640,\n",
    "    base_size=1024,\n",
    "    crop_mode=True,\n",
    "    save_results = True,\n",
    "    test_compress = False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f422JgM9sdVT"
   },
   "source": [
    "### Saving to float16 for VLLM\n",
    "\n",
    "We also support saving to `float16` directly. Select `merged_16bit` for float16. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iHjt_SMYsd3P"
   },
   "outputs": [],
   "source": [
    "# Select ONLY 1 to save! (Both not needed!)\n",
    "\n",
    "# Save locally to 16bit\n",
    "if False: model.save_pretrained_merged(\"unsloth_finetune\", tokenizer,)\n",
    "\n",
    "# To export and save to your Hugging Face account\n",
    "if False: model.push_to_hub_merged(\"YOUR_USERNAME/unsloth_finetune\", tokenizer, token = \"PUT_HERE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n",
    "\n",
    "Some other links:\n",
    "1. Train your own reasoning model - Llama GRPO notebook [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)\n",
    "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n",
    "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n",
    "6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n",
    "\n",
    "<div class=\"align-center\">\n",
    "  <a href=\"https://unsloth.ai\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
    "  <a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n",
    "  <a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a>\n",
    "\n",
    "  Join Discord if you need help + \u2b50\ufe0f <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> \u2b50\ufe0f\n",
    "\n",
    "  This notebook and all Unsloth notebooks are licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme)\n",
    "</div>\n"
   ]
  }
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